While epidemiologic and clinical research often aims to analyze predictors of specific endpoints, time-to-the-specific-event analysis can be hampered by problems with cause ascertainment. Under typical assumptions of competing risks analysis (and missing-data settings), we correct the cause-specific proportional hazards analysis when information on the reliability of diagnosis is available. Our method avoids bias in effect estimates at low cost in variance, thus offering a perspective for better-informed decision making. The ratio of different cause-specific hazards can be estimated flexibly for this purpose. It thus complements an all-cause analysis. In a sensitivity analysis, this approach can reveal the likely extent and direction of the bias of a standard cause-specific analysis when the diagnosis is suspect. These 2 uses are illustrated in a randomized vaccine trial and an epidemiologic cohort study, respectively.
From the aDepartment of Applied Mathematics and Computer Science, Ghent University, Ghent, Belgium; and bFaculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom.
Submitted 21 January 2011; accepted 4 November 2011.
Supported by the Institute for the Promotion of Innovation by Science and Technology in Flanders (IWT-Vlaanderen) through a research fellowship (B.V.R.). E.G. acknowledges support from the IAP research network grant nr. P06/03 from the Belgian government (Belgian Science Policy). The authors reported no other financial interests related to this research.
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Correspondence: Bart Van Rompaye, Department of Applied Mathematics and Computer Science WE02, Ghent University, Krijgslaan 281, S9, 9000 Ghent, Belgium. E-mail: email@example.com.